Segmentation of Extrapulmonary Tuberculosis Infection Using Modified Automatic Seeded Region Growing
© Avazpour et al. 2009
Received: 19 May 2009
Accepted: 22 June 2009
Published: 14 July 2009
In the image segmentation process of positron emission tomography combined with computed tomography (PET/CT) imaging, previous works used information in CT only for segmenting the image without utilizing the information that can be provided by PET. This paper proposes to utilize the hot spot values in PET to guide the segmentation in CT, in automatic image segmentation using seeded region growing (SRG) technique. This automatic segmentation routine can be used as part of automatic diagnostic tools. In addition to the original initial seed selection using hot spot values in PET, this paper also introduces a new SRG growing criterion, the sliding windows. Fourteen images of patients having extrapulmonary tuberculosis have been examined using the above-mentioned method. To evaluate the performance of the modified SRG, three fidelity criteria are measured: percentage of under-segmentation area, percentage of over-segmentation area, and average time consumption. In terms of the under-segmentation percentage, SRG with average of the region growing criterion shows the least error percentage (51.85%). Meanwhile, SRG with local averaging and variance yielded the best results (2.67%) for the over-segmentation percentage. In terms of the time complexity, the modified SRG with local averaging and variance growing criterion shows the best performance with 5.273 s average execution time. The results indicate that the proposed methods yield fairly good performance in terms of the over- and under-segmentation area. The results also demonstrated that the hot spot values in PET can be used to guide the automatic segmentation in CT image.
KeywordsSeeded Region Growing Segmentation Dual Modality Imaging Positron Emission Tomography Computed Tomography
Tuberculosis infection (TB) has been pandemic in countries within the tropical regions for decades . This airborne disease is easily spread through the air in tiny droplets discharged in a cough by a person suffering from active tuberculosis of the lungs known as pulmonary TB. TB can also affect other parts of body such as brain, bones, lymph nodes, kidneys, and skin. Thereby, it will be named as extrapulmonary tuberculosis (EPTB) infection .
Positron emission tomography combined with computed tomography (PET/CT) is opening its way in clinical applications, especially in cancer staging and post-therapy surveillance with expansion into infection and inflammation [3–8]. In this dual imaging modality, PET images provide information on metabolic activity of lesions, while CT images provide morphological information. This fusion technique has helped to increase the visual perception of images; however, diagnosing and analyzing these images is time consuming and a great challenge for experts. Furthermore, early lesion recognition will help determining the most effective treatment to be instituted to the patient. The purpose of this work is to introduce new automated segmentation framework which can help in better diagnosis of medical images, utilizing new dual imaging modalities. This framework can be used as part of automatic lesion detection and classification tools.
Since Adams and Bischof introduced seeded region growing (SRG) segmentation , the algorithm has been improving, and different growing criteria have been introduced. Approaches, like comparing the pixel to be added with the average intensity of the region grown at each step, maximum pixel value at region boundary, or maximum value of the pixels inside the region, tried to improve segmentation accuracy by controlling overgrowing to homogenous neighboring areas [9, 10]. Hojjatoleslami and Kittler introduced a new approach to SRG using average contrast and peripheral contrast to control the growing process and to make it predictable . Mehnert and Jackway improved SRG algorithm to be pixel-order-independent, processing pixels with same value in parallel, using priority queues . Wan and Higgins expanded SRG a bit further and introduced symmetric region growing based on line-by-line processing of the image, making segmentation less sensitive to the selection of initial seeds . All these algorithms require a starting point to begin segmentation procedure, and their authors have considered different routines for this purpose. Here, we are proposing a new way to start segmentation based on the data acquired from another imaging modality.
The problem with most unsupervised region growing algorithms is that it over-grows to homogeneous neighboring areas. On the other hand, in supervised region growing, the user must define the growing criteria (GC) to match image specifications. Therefore, for different images, certain GC will not always guarantee the best results. Here, we have described and proposed different aspects of GC for SRG algorithm to examine which results in better segmentation of organs in CT image using PET image data as starting point for the segmentation procedure.
The remainder of this paper is organized as follows. In section 2, proposed GC for SRG algorithm has been defined, and previously introduced GCs have been carefully examined. The segmentation results have been compared using over- and under-segmentation percentages and time complexity of each method in section 3. In section 4, we then discuss each algorithm's performance based on the acquired results.
2. Materials and Methods
In this study, patients were fasted overnight and injected with 18F-fluorodeoxyglucose (18F-FDG) radionuclide 45 min before the scan. Imaging studies were performed using Biograph 6, Siemens Medical Solutions Inc. PET/CT machine. Acquisition time was 3 min per bed position with seven bed positions covering from vertex to the mid-thigh. CT imaging was performed prior to PET imaging with patients in still position. A bolus injection of 100 ml of iodinated contrast media (Omnipaque 300, Amersham Health) was given intravenously. Acquisition parameters for six slices CT were 130 kV, 60 mAs, 0.8 s per CT rotation, 2.5 mm slice thickness, pitch 1.5.
New PET/CT devices come together on a single platform, and the patient will be imaged for both PET and CT at the same position, so there will be little patient movement [14, 15]. Both PET and CT images have been registered using cross-correlation and transformed to the position where they are best correlated .
2.1. Region Averaging
Threshold T is defined by the user to satisfy image specifications. User is asked to assign a threshold value which has the closest result to desired segmentation.
2.2. Local Averaging and Variance
Where Avg(M) is the average pixel value of the mask M and STD(M) is its standard deviation. At each step, pixels with the value within this GC will be added to the ROI. Growing process stops when there is no neighboring pixel that satisfies this criterion.
2.3. The Proposed Sliding Windows
Computed tomography images have various intensity properties, and different body lesions appear with different intensity; this calls the need for examining the specification of image before segmentation. CT images usually have 512 × 512 pixels dimension, so two local mask Ms (16 × 16 pixels) and Ml (64 × 64 pixels) centered at the seed point coordinate have been defined and average pixel value of both calculated. Considering these averages, we have:
If Avg(Ms)<Avg(Ml) then The area to be segmented is brighter than the surrounding area
If Avg(Ms)>Avg(Ml) then The area to be segmented is darker than the surrounding area
Else The segmentation area and surrounding have relatively same intensity
The percentage of sliding and the size of masks and W can be defined to satisfy the specification of the images to be examined. Here, when there is difference in average intensity of Ms and Ml, W has been slid by 25% of its size. W has been defined as a window of size 30. Region growing process will continue until there is no other bilinear neighboring pixel of the ROI with value falling within W.
In order to have a basis for segmentation evaluation, images have been sent to medical sources, and desired ROIs have been surveyed and selected manually by a certified radiologist. The manually selected ROIs have been set as benchmark data for optimum segmentation, and the segmentation results have to be compared with this benchmarked data.
To evaluate the effectiveness of the proposed methods, segmentation accuracy and time complexity have been considered. Segmentation accuracy has been tested based on calculation of over- and under-segmentation factors. Time complexity also has been defined by measuring the amount of time that each algorithm has consumed to finish its procedure.
As can be seen in Figure 6, SRG using local average and variance suffers from under-segmentation and SRG using average of region has the least under-segmentation error. This means that more areas of desired ROI will be covered using SRG with average of the region. On the other hand, considering over-segmentation errors in Figure 7, SRG using local averaging and variance presents the least over-segmentation errors. The proposed SRG using sliding windows deals with over-segmentation.
Image segmentation is a blind task, and there have been lots of researches to guide segmentation in a way that results in better precision ROI selection. Among segmentation algorithms, region growing highly depends on where the growing process starts and how to control it in order to avoid over-growing to homogenous neighboring areas . Therefore, we proposed the usage of dual modality imaging to use the data acquired from one modality to start the segmentation of images from another modality. Different aspects of GC have been tested to examine the efficiency of seed point selection from PET image on SRG segmentation.
Our effort here was to introduce automated segmentation methods which result in less errors and best performance. Considering the fact that the outputs are to be fed into automatic diagnostic tools, the segmented area must at least represent an estimate of the targeted organ in order for recognition algorithms to be able to recognize it. Therefore, less under-segmentation error is more desirable, bringing the fact that SRG using local averaging and variance cannot offer good results. If time complexity of the process is not an important issue, SRG using average of the region represents the most appreciated performance when guided by PET image data. Otherwise, the sliding windows can be chosen as the GC of choice for SRG segmentation.
This article has proposed a new scheme for automatic segmentation of dual modality medical images using seeded region growing. We proposed a new growing criterion to be used in SRG algorithm and compared its results with previously introduced criterions. Among the methods used here, SRG using region averaging is considered as supervised segmentation since it requires user involvement, and the rest are considered as unsupervised automatic segmentation.
- Guo N, Marra F, Marra C: Measuring health-related quality of life in tuberculosis: a systematic review. Health Quality Life Outcomes. 2009, 7: 14-10.1186/1477-7525-7-14.View ArticleGoogle Scholar
- Sharma SK, Mohan A: Extrapulmonary tuberculosis. Indian J Med Res. 2004, 120 (4): 316-353.PubMedGoogle Scholar
- Guo H, Zhu H, Xi Y, Zhang B, Li L, Huang Y: Diagnostic and prognostic value of 18F-FDG PET/CT for patients with suspected recurrence from squamous cell carcinoma of the esophagus. J Nucl Med. 2007, 48: 1251-1258. 10.2967/jnumed.106.036509.View ArticlePubMedGoogle Scholar
- Miller JC, Fischman AJ, Aquino SL, Blake MA, Thrall JH, Lee SI: FDG-PET CT for tumor imaging. J Am Coll Radiol. 2007, 4: 256-259. 10.1016/j.jacr.2006.10.011.View ArticlePubMedGoogle Scholar
- Goo JM, Im J, Do K, Yeo JS, Seo JB, Kim HY: Pulmonary tuberculoma evaluated by means of FDG PET: findings in 10 cases. Radiology. 2000, 216: 117-121.View ArticlePubMedGoogle Scholar
- Nguyen N, Chaar B, Osman M: Prevalence and patterns of soft tissue metastasis: detection with true whole-body F-18 FDG PET/CT. BMC Med Imaging. 2007, 7: 8-10.1186/1471-2342-7-8.PubMed CentralView ArticlePubMedGoogle Scholar
- Roedl JB, Prabhakar HB, Mueller PR, Colen RR, Blake MA: Prediction of metastatic disease and survival in patients with gastric and gastroesophageal junction tumors: the incremental value of PET-CT over PET and the clinical role of primary tumor volume measurements. Acad Radiol. 2009, 16: 218-226. 10.1016/j.acra.2008.06.004.View ArticlePubMedGoogle Scholar
- Caoili EM, Korobkin M, Brown RKJ, Mackie G, Shulkin BL: Differentiating adrenal adenomas from nonadenomas using 18F-FDG PET/CT: quantitative and qualitative evaluation. Acad Radiol. 2007, 14: 468-475. 10.1016/j.acra.2007.01.009.View ArticlePubMedGoogle Scholar
- Adams R, Bischof L: Seeded region growing, pattern analysis and machine intelligence. IEEE Trans Image Process. 1994, 16: 641-647.Google Scholar
- Hojjatoleslami SA, Kittler J: Automatic detection of calcification in mammograms, Image Processing and its Applications, 1995. Fifth International Conference on. 1995, 139-143.Google Scholar
- Hojjatoleslami SA, Kittler J: Region growing: a new approach, image processing. IEEE Trans Image Process. 1998, 7: 1079-1084. 10.1109/83.701170.View ArticlePubMedGoogle Scholar
- Mehnert A, Jackway P: An improved seeded region growing algorithm. Pattern Recognit Lett. 1997, 18: 1065-1071. 10.1016/S0167-8655(97)00131-1.View ArticleGoogle Scholar
- Wan Shu-Yen, Higgins WE: Symmetric region growing, Image Processing, 2000. Proceedings. 2000, 2: 96-99. International Conference on. 2Google Scholar
- Beyer T, Townsend DW, Brun T, Kinahan PE, Charron M, Roddy R: A combined PET/CT scanner for clinical oncology. J Nucl Med. 2000, 41: 1369-1379.PubMedGoogle Scholar
- Townsend DW, Beyer T: A combined PET/CT scanner: the path to true image fusion. Br J Radiol. 2002, 75: S24-S30.View ArticlePubMedGoogle Scholar
- Lewis J: Fast normalized cross-correlation. Vision Interface. 1995, 10: 120-123.Google Scholar
- Rohren EM, Turkington TG, Coleman RE: Clinical applications of PET in oncology. Radiology. 2004, 231: 305-332. 10.1148/radiol.2312021185.View ArticlePubMedGoogle Scholar
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